{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Comparison of predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- See preprocessing of gene expression and prediction were done in CaDRReS2/pipeline/* and 03_*\n",
    "- Convert predicted delta to cv to cell death percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:12.987647Z",
     "start_time": "2020-11-17T13:33:12.978008Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import metrics\n",
    "from collections import Counter\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('precision', 2)\n",
    "np.set_printoptions(suppress=True)\n",
    "from IPython.display import HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:12.993517Z",
     "start_time": "2020-11-17T13:33:12.989698Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "mpl.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:12.999152Z",
     "start_time": "2020-11-17T13:33:12.996125Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_shifted = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for experimental validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.004314Z",
     "start_time": "2020-11-17T13:33:13.001549Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_used = '3 fold' # All for HN, '9 fold' '3 fold' 'Median IC50' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for calculating % cell death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.009576Z",
     "start_time": "2020-11-17T13:33:13.006682Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_ref = 'log2_median_ic50_hn' # log2_median_ic50_hn | log2_median_ic50_3f_hn\n",
    "model_name = 'RWEN' # hn_drug_cw_dw10_100000_model | hn_drug_cw_dw1_100000_model | hn_drug_cw_dwsim10_100000_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.014701Z",
     "start_time": "2020-11-17T13:33:13.011833Z"
    }
   },
   "outputs": [],
   "source": [
    "current_dir = '../result/HN_model/TPM/'\n",
    "\n",
    "# current_dir = '../result/HN_model/TMM/'\n",
    "# current_dir = '../result/HN_model/TMM_p95/'\n",
    "# current_dir = '../result/HN_model/mat_norm/'\n",
    "# current_dir = '../result/HN_model/mat_norm_p95/'\n",
    "# current_dir = '../result/HN_model/mat_norm_log2_p95/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.032305Z",
     "start_time": "2020-11-17T13:33:13.016525Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "else:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}.csv'.format(dosage_ref, model_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.039638Z",
     "start_time": "2020-11-17T13:33:13.035170Z"
    }
   },
   "outputs": [],
   "source": [
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160', 'HN182']\n",
    "patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "single_drug_id_list = [1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "single_drug_list = ['Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "combi_drug_list = ['Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Gefitinib|Epothilone B'] + ['Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.050892Z",
     "start_time": "2020-11-17T13:33:13.042022Z"
    }
   },
   "outputs": [],
   "source": [
    "# read reference dosages file\n",
    "dosage_df = pd.read_csv('../preprocessed_data/GDSC/hn_drug_stat.csv', index_col=0)\n",
    "dosage_df = dosage_df.loc[single_drug_id_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Read experimental data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.176773Z",
     "start_time": "2020-11-17T13:33:13.052516Z"
    },
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File name</th>\n",
       "      <th>Dosage</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>1</td>\n",
       "      <td>56.98</td>\n",
       "      <td>64.74</td>\n",
       "      <td>40.52</td>\n",
       "      <td>26.77</td>\n",
       "      <td>97.41</td>\n",
       "      <td>67.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>2</td>\n",
       "      <td>58.10</td>\n",
       "      <td>64.20</td>\n",
       "      <td>38.30</td>\n",
       "      <td>25.42</td>\n",
       "      <td>96.90</td>\n",
       "      <td>68.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>3</td>\n",
       "      <td>53.09</td>\n",
       "      <td>59.52</td>\n",
       "      <td>35.23</td>\n",
       "      <td>24.12</td>\n",
       "      <td>93.86</td>\n",
       "      <td>66.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1</td>\n",
       "      <td>81.52</td>\n",
       "      <td>71.17</td>\n",
       "      <td>84.23</td>\n",
       "      <td>86.19</td>\n",
       "      <td>88.00</td>\n",
       "      <td>67.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>2</td>\n",
       "      <td>80.69</td>\n",
       "      <td>67.84</td>\n",
       "      <td>78.09</td>\n",
       "      <td>81.28</td>\n",
       "      <td>86.22</td>\n",
       "      <td>70.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               File name  Dosage       Drug  Replicate  HN120  \\\n",
       "0  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          1  56.98   \n",
       "1  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          2  58.10   \n",
       "2  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          3  53.09   \n",
       "6  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          1  81.52   \n",
       "7  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          2  80.69   \n",
       "\n",
       "   HN137  HN148  HN159  HN160  HN182  \n",
       "0  64.74  40.52  26.77  97.41  67.09  \n",
       "1  64.20  38.30  25.42  96.90  68.63  \n",
       "2  59.52  35.23  24.12  93.86  66.52  \n",
       "6  71.17  84.23  86.19  88.00  67.97  \n",
       "7  67.84  78.09  81.28  86.22  70.59  "
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Combined replicate #####\n",
    "\n",
    "validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "cv_single_df_list = []\n",
    "cv_combi_df_list = []\n",
    "\n",
    "for validation_fname in validation_fname_list:\n",
    "\n",
    "    cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "    cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_single_df = cv_single_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index() # just to rearrange columns\n",
    "\n",
    "    cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "    cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_combi_df = cv_combi_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index()\n",
    "    \n",
    "    cv_single_df_list += [cv_single_df]\n",
    "    cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.191371Z",
     "start_time": "2020-11-17T13:33:13.178797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File name</th>\n",
       "      <th>Dosage</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>Afatinib|Obatoclax Mesylate</td>\n",
       "      <td>2</td>\n",
       "      <td>22.71</td>\n",
       "      <td>32.61</td>\n",
       "      <td>11.69</td>\n",
       "      <td>14.09</td>\n",
       "      <td>67.42</td>\n",
       "      <td>26.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>13.54</td>\n",
       "      <td>5.65</td>\n",
       "      <td>9.36</td>\n",
       "      <td>7.59</td>\n",
       "      <td>32.98</td>\n",
       "      <td>17.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>2</td>\n",
       "      <td>14.10</td>\n",
       "      <td>5.68</td>\n",
       "      <td>8.73</td>\n",
       "      <td>7.46</td>\n",
       "      <td>34.33</td>\n",
       "      <td>16.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>PHA-793887|Trametinib</td>\n",
       "      <td>1</td>\n",
       "      <td>5.03</td>\n",
       "      <td>16.88</td>\n",
       "      <td>15.93</td>\n",
       "      <td>16.35</td>\n",
       "      <td>26.12</td>\n",
       "      <td>10.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>PHA-793887|Trametinib</td>\n",
       "      <td>2</td>\n",
       "      <td>4.14</td>\n",
       "      <td>15.13</td>\n",
       "      <td>11.70</td>\n",
       "      <td>15.93</td>\n",
       "      <td>25.38</td>\n",
       "      <td>9.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                File name       Dosage  \\\n",
       "25  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "26  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "27  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "28  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "29  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "\n",
       "                           Drug  Replicate  HN120  HN137  HN148  HN159  HN160  \\\n",
       "25  Afatinib|Obatoclax Mesylate          2  22.71  32.61  11.69  14.09  67.42   \n",
       "26          Epothilone B|PI-103          1  13.54   5.65   9.36   7.59  32.98   \n",
       "27          Epothilone B|PI-103          2  14.10   5.68   8.73   7.46  34.33   \n",
       "28        PHA-793887|Trametinib          1   5.03  16.88  15.93  16.35  26.12   \n",
       "29        PHA-793887|Trametinib          2   4.14  15.13  11.70  15.93  25.38   \n",
       "\n",
       "    HN182  \n",
       "25  26.53  \n",
       "26  17.34  \n",
       "27  16.63  \n",
       "28  10.35  \n",
       "29   9.85  "
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.196307Z",
     "start_time": "2020-11-17T13:33:13.193680Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "#### Check consistency across triplicate #####\n",
    "\n",
    "# validation_fname = validation_fname_list[0]\n",
    "\n",
    "# cv_new_df = pd.concat([pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2]), pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])], axis=0).reset_index()\n",
    "# cv_new_df = cv_new_df[~cv_new_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_new_df = cv_new_df.set_index(['Drug', 'Dosage', 'Replicate']).stack().reset_index()\n",
    "# cv_new_df.columns = ['Drug', 'Dosage', 'Replicate', 'Patient', 'Viability']\n",
    "# cv_new_df = cv_new_df.groupby(['Dosage', 'Drug', 'Patient', 'Replicate']).sum().unstack()\n",
    "\n",
    "# sns.pairplot(cv_new_df, plot_kws={'s':10, 'alpha':0.75, 'linewidth':0}, diag_kws={'bins':20}, aspect=1.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate % cell death at specific dosages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.209425Z",
     "start_time": "2020-11-17T13:33:13.198337Z"
    }
   },
   "outputs": [],
   "source": [
    "obs_kill_df = 100 - cv_df.set_index(['Dosage', 'Drug', 'File name', 'Replicate']).astype(float)\n",
    "obs_kill_df = obs_kill_df.loc[dosage_used]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.223888Z",
     "start_time": "2020-11-17T13:33:13.211044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Afatinib</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>47.25</td>\n",
       "      <td>34.18</td>\n",
       "      <td>55.59</td>\n",
       "      <td>73.87</td>\n",
       "      <td>3.39</td>\n",
       "      <td>27.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.07</td>\n",
       "      <td>28.62</td>\n",
       "      <td>58.39</td>\n",
       "      <td>73.48</td>\n",
       "      <td>5.57</td>\n",
       "      <td>30.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.24</td>\n",
       "      <td>31.68</td>\n",
       "      <td>57.19</td>\n",
       "      <td>74.51</td>\n",
       "      <td>4.97</td>\n",
       "      <td>30.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Docetaxel</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>49.10</td>\n",
       "      <td>61.99</td>\n",
       "      <td>31.87</td>\n",
       "      <td>35.45</td>\n",
       "      <td>26.92</td>\n",
       "      <td>56.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.15</td>\n",
       "      <td>58.86</td>\n",
       "      <td>36.26</td>\n",
       "      <td>29.41</td>\n",
       "      <td>26.08</td>\n",
       "      <td>54.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.05</td>\n",
       "      <td>58.84</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.61</td>\n",
       "      <td>25.09</td>\n",
       "      <td>58.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Doxorubicin</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>67.53</td>\n",
       "      <td>63.16</td>\n",
       "      <td>50.80</td>\n",
       "      <td>32.72</td>\n",
       "      <td>24.21</td>\n",
       "      <td>51.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64.43</td>\n",
       "      <td>62.38</td>\n",
       "      <td>54.29</td>\n",
       "      <td>29.53</td>\n",
       "      <td>23.48</td>\n",
       "      <td>52.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67.12</td>\n",
       "      <td>61.73</td>\n",
       "      <td>53.64</td>\n",
       "      <td>34.09</td>\n",
       "      <td>23.81</td>\n",
       "      <td>54.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B</th>\n",
       "      <th>validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>55.98</td>\n",
       "      <td>71.19</td>\n",
       "      <td>36.99</td>\n",
       "      <td>43.62</td>\n",
       "      <td>20.77</td>\n",
       "      <td>78.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              HN120  HN137  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          47.25  34.18   \n",
       "                                                   2          44.07  28.62   \n",
       "                                                   3          48.24  31.68   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          49.10  61.99   \n",
       "                                                   2          45.15  58.86   \n",
       "                                                   3          50.05  58.84   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          67.53  63.16   \n",
       "                                                   2          64.43  62.38   \n",
       "                                                   3          67.12  61.73   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          55.98  71.19   \n",
       "\n",
       "                                                              HN148  HN159  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          55.59  73.87   \n",
       "                                                   2          58.39  73.48   \n",
       "                                                   3          57.19  74.51   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          31.87  35.45   \n",
       "                                                   2          36.26  29.41   \n",
       "                                                   3          32.00  32.61   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          50.80  32.72   \n",
       "                                                   2          54.29  29.53   \n",
       "                                                   3          53.64  34.09   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          36.99  43.62   \n",
       "\n",
       "                                                              HN160  HN182  \n",
       "Drug         File name                             Replicate                \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1           3.39  27.10  \n",
       "                                                   2           5.57  30.12  \n",
       "                                                   3           4.97  30.80  \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          26.92  56.40  \n",
       "                                                   2          26.08  54.51  \n",
       "                                                   3          25.09  58.38  \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          24.21  51.81  \n",
       "                                                   2          23.48  52.38  \n",
       "                                                   3          23.81  54.06  \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          20.77  78.44  "
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_kill_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.248083Z",
     "start_time": "2020-11-17T13:33:13.226046Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_df = pred_single_df[pred_single_df['drug_id'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['drug_id_A'].isin(single_drug_id_list) & pred_combi_df['drug_id_B'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df.loc[:, 'Combi Name 1'] = pred_combi_df['drug_name_A'].values + '|' + pred_combi_df['drug_name_B'].values\n",
    "pred_combi_df.loc[:, 'Combi Name 2'] = pred_combi_df['drug_name_B'].values + '|' + pred_combi_df['drug_name_A'].values\n",
    "\n",
    "temp1 = pred_combi_df['Combi Name 1'][pred_combi_df['Combi Name 1'].isin(combi_drug_list)]\n",
    "temp2 = pred_combi_df['Combi Name 2'][pred_combi_df['Combi Name 2'].isin(combi_drug_list)]\n",
    "combi_name = pd.concat([temp1, temp2]).values\n",
    "\n",
    "pred_combi_df = pd.concat([pred_combi_df[pred_combi_df['Combi Name 1'].isin(combi_drug_list)], pred_combi_df[pred_combi_df['Combi Name 2'].isin(combi_drug_list)]])\n",
    "pred_combi_df.loc[:, 'Combi Name'] = combi_name\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['Combi Name'].isin(combi_drug_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.261856Z",
     "start_time": "2020-11-17T13:33:13.249722Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_delta</th>\n",
       "      <th>delta</th>\n",
       "      <th>cluster_kill</th>\n",
       "      <th>kill</th>\n",
       "      <th>drug_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>0.056986626834624|-0.5521462503121|0.532688663...</td>\n",
       "      <td>0.19</td>\n",
       "      <td>49.012625383709|59.452863251762|40.87266962319...</td>\n",
       "      <td>44.33</td>\n",
       "      <td>Docetaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>-2.8334341716821|-2.6720860912117|-3.778959838...</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>87.69637799287|86.437921582584|93.209818273866...</td>\n",
       "      <td>85.30</td>\n",
       "      <td>Doxorubicin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>-4.0213711434516|-3.6385077224033|-3.038971912...</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>94.199124702299|92.56712528151|89.152897129961...</td>\n",
       "      <td>87.04</td>\n",
       "      <td>Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1010</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>1.8712695534725|1.0992860185533|3.087297545393...</td>\n",
       "      <td>2.18</td>\n",
       "      <td>21.465935078167|31.821936069363|10.52739493194...</td>\n",
       "      <td>17.37</td>\n",
       "      <td>Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HN120</td>\n",
       "      <td>182</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>-1.690692592809|-1.3450796341807|-0.8269725475...</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>76.348806194752|71.754940580896|63.95043861070...</td>\n",
       "      <td>66.44</td>\n",
       "      <td>Obatoclax Mesylate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient  drug_id      cluster  \\\n",
       "0   HN120     1007  D1|D2|G1|G2   \n",
       "1   HN120      133  D1|D2|G1|G2   \n",
       "2   HN120      201  D1|D2|G1|G2   \n",
       "3   HN120     1010  D1|D2|G1|G2   \n",
       "4   HN120      182  D1|D2|G1|G2   \n",
       "\n",
       "                                           cluster_p  \\\n",
       "0  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "1  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "3  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "4  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "\n",
       "                                       cluster_delta  delta  \\\n",
       "0  0.056986626834624|-0.5521462503121|0.532688663...   0.19   \n",
       "1  -2.8334341716821|-2.6720860912117|-3.778959838...  -3.03   \n",
       "2  -4.0213711434516|-3.6385077224033|-3.038971912...  -3.32   \n",
       "3  1.8712695534725|1.0992860185533|3.087297545393...   2.18   \n",
       "4  -1.690692592809|-1.3450796341807|-0.8269725475...  -1.17   \n",
       "\n",
       "                                        cluster_kill   kill  \\\n",
       "0  49.012625383709|59.452863251762|40.87266962319...  44.33   \n",
       "1  87.69637799287|86.437921582584|93.209818273866...  85.30   \n",
       "2  94.199124702299|92.56712528151|89.152897129961...  87.04   \n",
       "3  21.465935078167|31.821936069363|10.52739493194...  17.37   \n",
       "4  76.348806194752|71.754940580896|63.95043861070...  66.44   \n",
       "\n",
       "            drug_name  \n",
       "0           Docetaxel  \n",
       "1         Doxorubicin  \n",
       "2        Epothilone B  \n",
       "3           Gefitinib  \n",
       "4  Obatoclax Mesylate  "
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_single_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.299161Z",
     "start_time": "2020-11-17T13:33:13.263923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id_A</th>\n",
       "      <th>drug_name_A</th>\n",
       "      <th>drug_id_B</th>\n",
       "      <th>drug_name_B</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_kill_A</th>\n",
       "      <th>cluster_kill_B</th>\n",
       "      <th>cluster_kill_C</th>\n",
       "      <th>kill_A</th>\n",
       "      <th>kill_B</th>\n",
       "      <th>kill_C</th>\n",
       "      <th>improve</th>\n",
       "      <th>improve_p</th>\n",
       "      <th>kill_entropy</th>\n",
       "      <th>sum_kill_dif</th>\n",
       "      <th>Combi Name 1</th>\n",
       "      <th>Combi Name 2</th>\n",
       "      <th>Combi Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>HN148</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>94.651287670146|91.781063189907|89.065482394711</td>\n",
       "      <td>84.303597186352|90.702409428612|97.809942266892</td>\n",
       "      <td>99.160444567363|99.235836906076|99.760527751607</td>\n",
       "      <td>89.45</td>\n",
       "      <td>90.00</td>\n",
       "      <td>97.31</td>\n",
       "      <td>7.31</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.01</td>\n",
       "      <td>20.17</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>HN160</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>73.748995316106|61.012871467064|79.355660888475</td>\n",
       "      <td>15.169279049775|82.923592297452|44.080401829425</td>\n",
       "      <td>77.731083469975|93.342398980199|88.455768523864</td>\n",
       "      <td>69.38</td>\n",
       "      <td>47.99</td>\n",
       "      <td>85.95</td>\n",
       "      <td>16.58</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.98</td>\n",
       "      <td>115.77</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>94.199124702299|92.56712528151|89.152897129961...</td>\n",
       "      <td>88.493859956725|98.383266503135|98.14105898102...</td>\n",
       "      <td>99.332543164511|99.879830224646|99.79835875537...</td>\n",
       "      <td>87.04</td>\n",
       "      <td>89.75</td>\n",
       "      <td>94.68</td>\n",
       "      <td>4.93</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.94</td>\n",
       "      <td>23.93</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>HN182</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>72.742297809287|71.895837200163|79.355660888475</td>\n",
       "      <td>81.003681614505|58.771620604048|44.080401829425</td>\n",
       "      <td>94.822040107282|88.413109134827|88.455768523864</td>\n",
       "      <td>73.09</td>\n",
       "      <td>73.60</td>\n",
       "      <td>93.03</td>\n",
       "      <td>19.42</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.86</td>\n",
       "      <td>56.66</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>49.012625383709|59.452863251762|40.87266962319...</td>\n",
       "      <td>94.199124702299|92.56712528151|89.152897129961...</td>\n",
       "      <td>97.042285980937|96.986182123569|93.58639764972...</td>\n",
       "      <td>44.33</td>\n",
       "      <td>87.04</td>\n",
       "      <td>90.67</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>178.64</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>HN160</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>30.588373050042|40.505355801517|21.430055207669</td>\n",
       "      <td>73.748995316106|61.012871467064|79.355660888475</td>\n",
       "      <td>81.7787505582|76.804746596124|83.779754157333</td>\n",
       "      <td>33.21</td>\n",
       "      <td>69.38</td>\n",
       "      <td>80.04</td>\n",
       "      <td>10.66</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>121.59</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>HN160</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>73.748995316106|61.012871467064|79.355660888475</td>\n",
       "      <td>4.3923975519164|3.7194562159516|20.045752217391</td>\n",
       "      <td>74.902043803195|62.462980642703|83.493973953689</td>\n",
       "      <td>69.38</td>\n",
       "      <td>6.66</td>\n",
       "      <td>71.14</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>185.96</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>HN159</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>78.823803391628|68.258284928745|66.293253736069</td>\n",
       "      <td>9.6447844446245|6.8378862967677|6.9810868224242</td>\n",
       "      <td>80.866201908075|70.428747313961|68.646350957768</td>\n",
       "      <td>69.84</td>\n",
       "      <td>7.72</td>\n",
       "      <td>72.03</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>189.91</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>HN148</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>94.651287670146|91.781063189907|89.065482394711</td>\n",
       "      <td>7.5810053997229|11.051626753858|22.842970548</td>\n",
       "      <td>95.056773840688|92.689389409294|91.563251030853</td>\n",
       "      <td>89.45</td>\n",
       "      <td>15.14</td>\n",
       "      <td>90.91</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>234.02</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>HN137</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>95.44253154549|95.999007952804|96.184901134623...</td>\n",
       "      <td>32.579167038043|26.244513394081|20.22939550584...</td>\n",
       "      <td>96.927316805991|97.049048846526|96.95667257303...</td>\n",
       "      <td>92.50</td>\n",
       "      <td>21.66</td>\n",
       "      <td>93.66</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>439.79</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>94.199124702299|92.56712528151|89.152897129961...</td>\n",
       "      <td>21.465935078167|31.821936069363|10.52739493194...</td>\n",
       "      <td>95.444336827669|94.932409922544|90.29481448776...</td>\n",
       "      <td>87.04</td>\n",
       "      <td>17.37</td>\n",
       "      <td>88.38</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>290.12</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>HN182</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>90.021009903416|76.516714589749|82.435639317301</td>\n",
       "      <td>76.613736396225|71.279199848479|64.562998866947</td>\n",
       "      <td>97.666287071018|93.255412528311|93.775717305858</td>\n",
       "      <td>86.69</td>\n",
       "      <td>74.59</td>\n",
       "      <td>96.47</td>\n",
       "      <td>9.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.52</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>HN182</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>38.102720137739|45.630260578849|21.430055207669</td>\n",
       "      <td>9.1191197214381|11.512069671947|20.045752217391</td>\n",
       "      <td>43.747207192692|51.889342861468|37.179991658081</td>\n",
       "      <td>38.31</td>\n",
       "      <td>10.46</td>\n",
       "      <td>44.88</td>\n",
       "      <td>6.56</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>64.49</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>HN182</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>38.102720137739|45.630260578849|21.430055207669</td>\n",
       "      <td>72.742297809287|71.895837200163|79.355660888475</td>\n",
       "      <td>83.128223790993|84.719839919233|83.779754157333</td>\n",
       "      <td>38.31</td>\n",
       "      <td>73.09</td>\n",
       "      <td>83.50</td>\n",
       "      <td>10.41</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.83</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>HN160</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>88.962402807547|90.825419874007|82.435639317301</td>\n",
       "      <td>64.932114748688|59.661146985897|64.562998866947</td>\n",
       "      <td>96.129348082049|96.299079608315|93.775717305858</td>\n",
       "      <td>88.67</td>\n",
       "      <td>62.69</td>\n",
       "      <td>95.82</td>\n",
       "      <td>7.14</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>73.07</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>HN160</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>30.588373050042|40.505355801517|21.430055207669</td>\n",
       "      <td>4.3923975519164|3.7194562159516|20.045752217391</td>\n",
       "      <td>33.637207652937|42.718233043316|37.179991658081</td>\n",
       "      <td>33.21</td>\n",
       "      <td>6.66</td>\n",
       "      <td>37.98</td>\n",
       "      <td>4.77</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>64.37</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>HN159</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>78.823803391628|68.258284928745|66.293253736069</td>\n",
       "      <td>97.753334496561|92.769131421538|95.671962791641</td>\n",
       "      <td>99.524241695859|97.704798298648|98.54115947997</td>\n",
       "      <td>69.84</td>\n",
       "      <td>94.65</td>\n",
       "      <td>97.52</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72.82</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>49.012625383709|59.452863251762|40.87266962319...</td>\n",
       "      <td>21.465935078167|31.821936069363|10.52739493194...</td>\n",
       "      <td>59.957542116904|72.355747185743|47.09723720467...</td>\n",
       "      <td>44.33</td>\n",
       "      <td>17.37</td>\n",
       "      <td>53.05</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>111.48</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>HN159</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>86.774331456483|89.474189774705|88.967438294023</td>\n",
       "      <td>68.275975425386|76.417342108825|73.486365185784</td>\n",
       "      <td>95.804285661098|97.517734184294|97.074866878624</td>\n",
       "      <td>87.30</td>\n",
       "      <td>71.50</td>\n",
       "      <td>95.59</td>\n",
       "      <td>8.29</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.04</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>HN159</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>24.178112169536|22.181084666833|24.468976321739</td>\n",
       "      <td>9.6447844446245|6.8378862967677|6.9810868224242</td>\n",
       "      <td>31.490969812629|27.502253614693|29.741862662584</td>\n",
       "      <td>23.66</td>\n",
       "      <td>7.72</td>\n",
       "      <td>29.53</td>\n",
       "      <td>5.87</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.36</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>HN159</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>24.178112169536|22.181084666833|24.468976321739</td>\n",
       "      <td>78.823803391628|68.258284928745|66.293253736069</td>\n",
       "      <td>83.943807960842|75.298941623405|74.540949498219</td>\n",
       "      <td>23.66</td>\n",
       "      <td>69.84</td>\n",
       "      <td>76.77</td>\n",
       "      <td>6.93</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>142.55</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>HN148</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>93.065282794362|92.998715518616|83.622193782922</td>\n",
       "      <td>74.908097650698|78.173596412407|75.92871202555</td>\n",
       "      <td>98.259947530561|98.471871392778|96.05765110159</td>\n",
       "      <td>86.70</td>\n",
       "      <td>74.43</td>\n",
       "      <td>95.17</td>\n",
       "      <td>8.47</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.68</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>HN148</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>53.428019724419|42.565291444729|47.912317244552</td>\n",
       "      <td>7.5810053997229|11.051626753858|22.842970548</td>\n",
       "      <td>56.958644063869|48.912761061424|59.810691275515</td>\n",
       "      <td>47.51</td>\n",
       "      <td>15.14</td>\n",
       "      <td>55.38</td>\n",
       "      <td>7.88</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>102.43</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>HN148</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>53.428019724419|42.565291444729|47.912317244552</td>\n",
       "      <td>94.651287670146|91.781063189907|89.065482394711</td>\n",
       "      <td>97.508998748743|95.279477596781|94.304463158918</td>\n",
       "      <td>47.51</td>\n",
       "      <td>89.45</td>\n",
       "      <td>93.47</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>131.59</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>HN137</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>95.44253154549|95.999007952804|96.184901134623...</td>\n",
       "      <td>88.271399091043|69.1407017213|62.2454743811|91...</td>\n",
       "      <td>99.465472713419|98.765321930049|98.55962752148...</td>\n",
       "      <td>92.50</td>\n",
       "      <td>79.54</td>\n",
       "      <td>97.31</td>\n",
       "      <td>4.80</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125.35</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>HN137</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>83.325642371615|89.137612549776|75.44527042929...</td>\n",
       "      <td>76.305100277761|64.772748292351|65.86817844533...</td>\n",
       "      <td>96.049027680627|96.173479431449|91.61902351969...</td>\n",
       "      <td>83.76</td>\n",
       "      <td>69.37</td>\n",
       "      <td>94.04</td>\n",
       "      <td>10.27</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.91</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>36.582875652674|29.8424353741|28.016510025631|...</td>\n",
       "      <td>32.579167038043|26.244513394081|20.22939550584...</td>\n",
       "      <td>57.243646524513|48.254946819305|42.57833491145...</td>\n",
       "      <td>39.87</td>\n",
       "      <td>21.66</td>\n",
       "      <td>53.23</td>\n",
       "      <td>13.36</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104.19</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>36.582875652674|29.8424353741|28.016510025631|...</td>\n",
       "      <td>95.44253154549|95.999007952804|96.184901134623...</td>\n",
       "      <td>97.109784563113|97.193001418811|97.25375869072...</td>\n",
       "      <td>39.87</td>\n",
       "      <td>92.50</td>\n",
       "      <td>94.99</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>335.60</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>87.69637799287|86.437921582584|93.209818273866...</td>\n",
       "      <td>74.651824912916|71.873099117706|61.64723300190...</td>\n",
       "      <td>96.88125635158|96.185407645954|97.395777423828...</td>\n",
       "      <td>85.30</td>\n",
       "      <td>65.00</td>\n",
       "      <td>92.11</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>83.16</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>HN182</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>72.742297809287|71.895837200163|79.355660888475</td>\n",
       "      <td>9.1191197214381|11.512069671947|20.045752217391</td>\n",
       "      <td>75.227960305371|75.131208002398|83.493973953689</td>\n",
       "      <td>73.09</td>\n",
       "      <td>10.46</td>\n",
       "      <td>75.86</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>183.32</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    patient  drug_id_A   drug_name_A  drug_id_B   drug_name_B  \\\n",
       "72    HN148        201  Epothilone B        302        PI-103   \n",
       "128   HN160        201  Epothilone B        302        PI-103   \n",
       "16    HN120        201  Epothilone B        302        PI-103   \n",
       "156   HN182        201  Epothilone B        302        PI-103   \n",
       "1     HN120       1007     Docetaxel        201  Epothilone B   \n",
       "113   HN160       1007     Docetaxel        201  Epothilone B   \n",
       "125   HN160        201  Epothilone B       1010     Gefitinib   \n",
       "97    HN159        201  Epothilone B       1010     Gefitinib   \n",
       "69    HN148        201  Epothilone B       1010     Gefitinib   \n",
       "41    HN137        201  Epothilone B       1010     Gefitinib   \n",
       "13    HN120        201  Epothilone B       1010     Gefitinib   \n",
       "152   HN182        133   Doxorubicin       1012    Vorinostat   \n",
       "142   HN182       1007     Docetaxel       1010     Gefitinib   \n",
       "141   HN182       1007     Docetaxel        201  Epothilone B   \n",
       "124   HN160        133   Doxorubicin       1012    Vorinostat   \n",
       "114   HN160       1007     Docetaxel       1010     Gefitinib   \n",
       "100   HN159        201  Epothilone B        302        PI-103   \n",
       "2     HN120       1007     Docetaxel       1010     Gefitinib   \n",
       "96    HN159        133   Doxorubicin       1012    Vorinostat   \n",
       "86    HN159       1007     Docetaxel       1010     Gefitinib   \n",
       "85    HN159       1007     Docetaxel        201  Epothilone B   \n",
       "68    HN148        133   Doxorubicin       1012    Vorinostat   \n",
       "58    HN148       1007     Docetaxel       1010     Gefitinib   \n",
       "57    HN148       1007     Docetaxel        201  Epothilone B   \n",
       "44    HN137        201  Epothilone B        302        PI-103   \n",
       "40    HN137        133   Doxorubicin       1012    Vorinostat   \n",
       "30    HN137       1007     Docetaxel       1010     Gefitinib   \n",
       "29    HN137       1007     Docetaxel        201  Epothilone B   \n",
       "12    HN120        133   Doxorubicin       1012    Vorinostat   \n",
       "153   HN182        201  Epothilone B       1010     Gefitinib   \n",
       "\n",
       "               cluster                                          cluster_p  \\\n",
       "72            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "128            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "16         D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "156            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "1          D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "113            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "125            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "97            I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "69            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "41   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "13         D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "152            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "142            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "141            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "124            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "114            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "100           I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "2          D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "96            I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "86            I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "85            I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "68            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "58            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "57            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "44   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "40   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "30   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "29   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "12         D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "153            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "\n",
       "                                        cluster_kill_A  \\\n",
       "72     94.651287670146|91.781063189907|89.065482394711   \n",
       "128    73.748995316106|61.012871467064|79.355660888475   \n",
       "16   94.199124702299|92.56712528151|89.152897129961...   \n",
       "156    72.742297809287|71.895837200163|79.355660888475   \n",
       "1    49.012625383709|59.452863251762|40.87266962319...   \n",
       "113    30.588373050042|40.505355801517|21.430055207669   \n",
       "125    73.748995316106|61.012871467064|79.355660888475   \n",
       "97     78.823803391628|68.258284928745|66.293253736069   \n",
       "69     94.651287670146|91.781063189907|89.065482394711   \n",
       "41   95.44253154549|95.999007952804|96.184901134623...   \n",
       "13   94.199124702299|92.56712528151|89.152897129961...   \n",
       "152    90.021009903416|76.516714589749|82.435639317301   \n",
       "142    38.102720137739|45.630260578849|21.430055207669   \n",
       "141    38.102720137739|45.630260578849|21.430055207669   \n",
       "124    88.962402807547|90.825419874007|82.435639317301   \n",
       "114    30.588373050042|40.505355801517|21.430055207669   \n",
       "100    78.823803391628|68.258284928745|66.293253736069   \n",
       "2    49.012625383709|59.452863251762|40.87266962319...   \n",
       "96     86.774331456483|89.474189774705|88.967438294023   \n",
       "86     24.178112169536|22.181084666833|24.468976321739   \n",
       "85     24.178112169536|22.181084666833|24.468976321739   \n",
       "68     93.065282794362|92.998715518616|83.622193782922   \n",
       "58     53.428019724419|42.565291444729|47.912317244552   \n",
       "57     53.428019724419|42.565291444729|47.912317244552   \n",
       "44   95.44253154549|95.999007952804|96.184901134623...   \n",
       "40   83.325642371615|89.137612549776|75.44527042929...   \n",
       "30   36.582875652674|29.8424353741|28.016510025631|...   \n",
       "29   36.582875652674|29.8424353741|28.016510025631|...   \n",
       "12   87.69637799287|86.437921582584|93.209818273866...   \n",
       "153    72.742297809287|71.895837200163|79.355660888475   \n",
       "\n",
       "                                        cluster_kill_B  \\\n",
       "72     84.303597186352|90.702409428612|97.809942266892   \n",
       "128    15.169279049775|82.923592297452|44.080401829425   \n",
       "16   88.493859956725|98.383266503135|98.14105898102...   \n",
       "156    81.003681614505|58.771620604048|44.080401829425   \n",
       "1    94.199124702299|92.56712528151|89.152897129961...   \n",
       "113    73.748995316106|61.012871467064|79.355660888475   \n",
       "125    4.3923975519164|3.7194562159516|20.045752217391   \n",
       "97     9.6447844446245|6.8378862967677|6.9810868224242   \n",
       "69        7.5810053997229|11.051626753858|22.842970548   \n",
       "41   32.579167038043|26.244513394081|20.22939550584...   \n",
       "13   21.465935078167|31.821936069363|10.52739493194...   \n",
       "152    76.613736396225|71.279199848479|64.562998866947   \n",
       "142    9.1191197214381|11.512069671947|20.045752217391   \n",
       "141    72.742297809287|71.895837200163|79.355660888475   \n",
       "124    64.932114748688|59.661146985897|64.562998866947   \n",
       "114    4.3923975519164|3.7194562159516|20.045752217391   \n",
       "100    97.753334496561|92.769131421538|95.671962791641   \n",
       "2    21.465935078167|31.821936069363|10.52739493194...   \n",
       "96     68.275975425386|76.417342108825|73.486365185784   \n",
       "86     9.6447844446245|6.8378862967677|6.9810868224242   \n",
       "85     78.823803391628|68.258284928745|66.293253736069   \n",
       "68      74.908097650698|78.173596412407|75.92871202555   \n",
       "58        7.5810053997229|11.051626753858|22.842970548   \n",
       "57     94.651287670146|91.781063189907|89.065482394711   \n",
       "44   88.271399091043|69.1407017213|62.2454743811|91...   \n",
       "40   76.305100277761|64.772748292351|65.86817844533...   \n",
       "30   32.579167038043|26.244513394081|20.22939550584...   \n",
       "29   95.44253154549|95.999007952804|96.184901134623...   \n",
       "12   74.651824912916|71.873099117706|61.64723300190...   \n",
       "153    9.1191197214381|11.512069671947|20.045752217391   \n",
       "\n",
       "                                        cluster_kill_C  kill_A  kill_B  \\\n",
       "72     99.160444567363|99.235836906076|99.760527751607   89.45   90.00   \n",
       "128    77.731083469975|93.342398980199|88.455768523864   69.38   47.99   \n",
       "16   99.332543164511|99.879830224646|99.79835875537...   87.04   89.75   \n",
       "156    94.822040107282|88.413109134827|88.455768523864   73.09   73.60   \n",
       "1    97.042285980937|96.986182123569|93.58639764972...   44.33   87.04   \n",
       "113      81.7787505582|76.804746596124|83.779754157333   33.21   69.38   \n",
       "125    74.902043803195|62.462980642703|83.493973953689   69.38    6.66   \n",
       "97     80.866201908075|70.428747313961|68.646350957768   69.84    7.72   \n",
       "69     95.056773840688|92.689389409294|91.563251030853   89.45   15.14   \n",
       "41   96.927316805991|97.049048846526|96.95667257303...   92.50   21.66   \n",
       "13   95.444336827669|94.932409922544|90.29481448776...   87.04   17.37   \n",
       "152    97.666287071018|93.255412528311|93.775717305858   86.69   74.59   \n",
       "142    43.747207192692|51.889342861468|37.179991658081   38.31   10.46   \n",
       "141    83.128223790993|84.719839919233|83.779754157333   38.31   73.09   \n",
       "124    96.129348082049|96.299079608315|93.775717305858   88.67   62.69   \n",
       "114    33.637207652937|42.718233043316|37.179991658081   33.21    6.66   \n",
       "100     99.524241695859|97.704798298648|98.54115947997   69.84   94.65   \n",
       "2    59.957542116904|72.355747185743|47.09723720467...   44.33   17.37   \n",
       "96     95.804285661098|97.517734184294|97.074866878624   87.30   71.50   \n",
       "86     31.490969812629|27.502253614693|29.741862662584   23.66    7.72   \n",
       "85     83.943807960842|75.298941623405|74.540949498219   23.66   69.84   \n",
       "68      98.259947530561|98.471871392778|96.05765110159   86.70   74.43   \n",
       "58     56.958644063869|48.912761061424|59.810691275515   47.51   15.14   \n",
       "57     97.508998748743|95.279477596781|94.304463158918   47.51   89.45   \n",
       "44   99.465472713419|98.765321930049|98.55962752148...   92.50   79.54   \n",
       "40   96.049027680627|96.173479431449|91.61902351969...   83.76   69.37   \n",
       "30   57.243646524513|48.254946819305|42.57833491145...   39.87   21.66   \n",
       "29   97.109784563113|97.193001418811|97.25375869072...   39.87   92.50   \n",
       "12   96.88125635158|96.185407645954|97.395777423828...   85.30   65.00   \n",
       "153    75.227960305371|75.131208002398|83.493973953689   73.09   10.46   \n",
       "\n",
       "     kill_C  improve  improve_p  kill_entropy  sum_kill_dif  \\\n",
       "72    97.31     7.31       0.08          1.01         20.17   \n",
       "128   85.95    16.58       0.24          0.98        115.77   \n",
       "16    94.68     4.93       0.05          0.94         23.93   \n",
       "156   93.03    19.42       0.26          0.86         56.66   \n",
       "1     90.67     3.63       0.04          0.00        178.64   \n",
       "113   80.04    10.66       0.15          0.00        121.59   \n",
       "125   71.14     1.76       0.03          0.00        185.96   \n",
       "97    72.03     2.19       0.03          0.00        189.91   \n",
       "69    90.91     1.46       0.02          0.00        234.02   \n",
       "41    93.66     1.16       0.01          0.00        439.79   \n",
       "13    88.38     1.34       0.02          0.00        290.12   \n",
       "152   96.47     9.77       0.11          0.00         36.52   \n",
       "142   44.88     6.56       0.17          0.00         64.49   \n",
       "141   83.50    10.41       0.14          0.00        118.83   \n",
       "124   95.82     7.14       0.08          0.00         73.07   \n",
       "114   37.98     4.77       0.14          0.00         64.37   \n",
       "100   97.52     2.87       0.03          0.00         72.82   \n",
       "2     53.05     8.72       0.20          0.00        111.48   \n",
       "96    95.59     8.29       0.09          0.00         47.04   \n",
       "86    29.53     5.87       0.25          0.00         47.36   \n",
       "85    76.77     6.93       0.10          0.00        142.55   \n",
       "68    95.17     8.47       0.10          0.00         40.68   \n",
       "58    55.38     7.88       0.17          0.00        102.43   \n",
       "57    93.47     4.02       0.04          0.00        131.59   \n",
       "44    97.31     4.80       0.05          0.00        125.35   \n",
       "40    94.04    10.27       0.12          0.00         99.91   \n",
       "30    53.23    13.36       0.34          0.00        104.19   \n",
       "29    94.99     2.48       0.03          0.00        335.60   \n",
       "12    92.11     6.81       0.08          0.00         83.16   \n",
       "153   75.86     2.77       0.04          0.00        183.32   \n",
       "\n",
       "               Combi Name 1            Combi Name 2              Combi Name  \n",
       "72      Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "128     Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "16      Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "156     Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "1    Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "113  Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "125  Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "97   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "69   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "41   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "13   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "152  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "142     Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "141  Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "124  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "114     Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "100     Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "2       Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "96   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "86      Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "85   Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "68   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "58      Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "57   Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "44      Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "40   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "30      Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "29   Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "12   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "153  Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  "
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_df.sort_values('kill_entropy', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.322765Z",
     "start_time": "2020-11-17T13:33:13.301180Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_kill_df = pred_single_df[['patient', 'drug_name', 'kill']].pivot(index='drug_name', columns='patient', values='kill')\n",
    "pred_single_delta_df = pred_single_df[['patient', 'drug_name', 'delta']].pivot(index='drug_name', columns='patient', values='delta')\n",
    "pred_combi_kill_df = pred_combi_df[['patient', 'Combi Name', 'kill_C']].pivot(index='Combi Name', columns='patient', values='kill_C')\n",
    "\n",
    "obs_single_kill_df = obs_kill_df.loc[single_drug_list, patient_list]\n",
    "obs_combi_kill_df = obs_kill_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.334214Z",
     "start_time": "2020-11-17T13:33:13.325741Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.25)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "cmap = plt.cm.get_cmap('tab10', 10)\n",
    "colors = cmap(np.linspace(0, 1, 10))\n",
    "patient_color_dict = dict(zip(patient_list, colors[0:len(patient_list)]))\n",
    "drug_color_dict = dict(zip(single_drug_list, colors[0:len(single_drug_list)]))\n",
    "combi_color_dict = dict(zip(combi_drug_list, colors[0:len(combi_drug_list)]))\n",
    "\n",
    "drug_marker_dict = dict(zip(single_drug_list, ['o', 'v', '^', '<', '>', 'd', 's']))\n",
    "combi_marker_dict = dict(zip(combi_drug_list, ['p', '*', 'P', 'X', 'h']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For single drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.369522Z",
     "start_time": "2020-11-17T13:33:13.337223Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125, 5)\n",
      "(125, 3)\n",
      "(125, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>76.42</td>\n",
       "      <td>89.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>79.03</td>\n",
       "      <td>79.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>84.00</td>\n",
       "      <td>90.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>86.40</td>\n",
       "      <td>94.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>56.89</td>\n",
       "      <td>47.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug                              File name Replicate Patient  \\\n",
       "120  PI-103  validation_replicates_2019_06_24.xlsx         2   HN120   \n",
       "121  PI-103  validation_replicates_2019_06_24.xlsx         2   HN137   \n",
       "122  PI-103  validation_replicates_2019_06_24.xlsx         2   HN148   \n",
       "123  PI-103  validation_replicates_2019_06_24.xlsx         2   HN159   \n",
       "124  PI-103  validation_replicates_2019_06_24.xlsx         2   HN160   \n",
       "\n",
       "     Observed % cell death  Predicted % cell death  \n",
       "120                  76.42                   89.75  \n",
       "121                  79.03                   79.54  \n",
       "122                  84.00                   90.00  \n",
       "123                  86.40                   94.65  \n",
       "124                  56.89                   47.99  "
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "print (obs_df.shape)\n",
    "\n",
    "pred_df = pred_single_kill_df.loc[[d[0] for d in obs_single_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug', 'Patient', 'Predicted % cell death']\n",
    "print (pred_df.shape)\n",
    "\n",
    "scatter_single_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_single_df.loc[:, 'Replicate'] = scatter_single_df['Replicate'].astype(str)\n",
    "print (scatter_single_df.shape)\n",
    "scatter_single_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.415728Z",
     "start_time": "2020-11-17T13:33:13.371211Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Observed % cell death</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Predicted % cell death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>Patient</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Docetaxel</th>\n",
       "      <th>HN120</th>\n",
       "      <td>45.15</td>\n",
       "      <td>58.95</td>\n",
       "      <td>50.05</td>\n",
       "      <td>44.33</td>\n",
       "      <td>44.33</td>\n",
       "      <td>44.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN137</th>\n",
       "      <td>58.84</td>\n",
       "      <td>63.36</td>\n",
       "      <td>61.99</td>\n",
       "      <td>39.87</td>\n",
       "      <td>39.87</td>\n",
       "      <td>39.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN148</th>\n",
       "      <td>31.87</td>\n",
       "      <td>52.10</td>\n",
       "      <td>36.26</td>\n",
       "      <td>47.51</td>\n",
       "      <td>47.51</td>\n",
       "      <td>47.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN159</th>\n",
       "      <td>29.41</td>\n",
       "      <td>48.12</td>\n",
       "      <td>35.45</td>\n",
       "      <td>23.66</td>\n",
       "      <td>23.66</td>\n",
       "      <td>23.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN160</th>\n",
       "      <td>18.96</td>\n",
       "      <td>26.92</td>\n",
       "      <td>25.09</td>\n",
       "      <td>33.21</td>\n",
       "      <td>33.21</td>\n",
       "      <td>33.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Observed % cell death               Predicted % cell death  \\\n",
       "                                    min    max median                    min   \n",
       "Drug      Patient                                                              \n",
       "Docetaxel HN120                   45.15  58.95  50.05                  44.33   \n",
       "          HN137                   58.84  63.36  61.99                  39.87   \n",
       "          HN148                   31.87  52.10  36.26                  47.51   \n",
       "          HN159                   29.41  48.12  35.45                  23.66   \n",
       "          HN160                   18.96  26.92  25.09                  33.21   \n",
       "\n",
       "                                 \n",
       "                     max median  \n",
       "Drug      Patient                \n",
       "Docetaxel HN120    44.33  44.33  \n",
       "          HN137    39.87  39.87  \n",
       "          HN148    47.51  47.51  \n",
       "          HN159    23.66  23.66  \n",
       "          HN160    33.21  33.21  "
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_single_df.groupby(['Drug', 'Patient']).agg(['min', 'max', 'median']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.421096Z",
     "start_time": "2020-11-17T13:33:13.418092Z"
    }
   },
   "outputs": [],
   "source": [
    "# scatter_single_df = scatter_single_df[scatter_single_df['Patient'].isin(['HN137'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.852721Z",
     "start_time": "2020-11-17T13:33:13.426071Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single drug | Pearson r = 0.31 (4.78e-02)\n",
      "Single drug [R-sq -63.87%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Patient', style='Drug', s=150, alpha=0.9, ax=ax)\n",
    "\n",
    "for _, row in scatter_single_df.groupby(['Drug', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "    \n",
    "\n",
    "vmin = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Single drug | Pearson r = {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Single drug [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "elif dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4E_single_drug_{}_{}.svg'.format(dosage_used, model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.858325Z",
     "start_time": "2020-11-17T13:33:13.855910Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# drug_list = sorted(list(set(scatter_df['Drug'])))\n",
    "# for drug in drug_list:\n",
    "#     d_y = scatter_df[scatter_df['Drug']==drug]['Observed % cell death'].values\n",
    "#     d_y_hat = scatter_df[scatter_df['Drug']==drug]['Predicted % cell death'].values\n",
    "#     print (\"{} [R-sq = {:.2f}%]\".format(drug, metrics.r2_score(d_y, d_y_hat)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.863114Z",
     "start_time": "2020-11-17T13:33:13.860300Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# patient_list = sorted(list(set(scatter_df['Patient'])))\n",
    "# for patient in patient_list:\n",
    "#     d_y = scatter_df[scatter_df['Patient']==patient]['Observed % cell death'].values\n",
    "#     d_y_hat = scatter_df[scatter_df['Patient']==patient]['Predicted % cell death'].values\n",
    "#     print (\"{} [R-sq = {:.2f}%]\".format(patient, metrics.r2_score(d_y, d_y_hat)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For combi drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:13.891847Z",
     "start_time": "2020-11-17T13:33:13.865053Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 5)\n",
      "(70, 3)\n",
      "(70, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>85.90</td>\n",
       "      <td>94.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>94.32</td>\n",
       "      <td>97.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>91.27</td>\n",
       "      <td>97.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>92.54</td>\n",
       "      <td>97.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>65.67</td>\n",
       "      <td>85.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug combination                              File name Replicate  \\\n",
       "65  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "66  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "67  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "68  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "69  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "\n",
       "   Patient  Observed % cell death  Predicted % cell death  \n",
       "65   HN120                  85.90                   94.68  \n",
       "66   HN137                  94.32                   97.31  \n",
       "67   HN148                  91.27                   97.31  \n",
       "68   HN159                  92.54                   97.52  \n",
       "69   HN160                  65.67                   85.95  "
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_combi_kill_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "print (obs_df.shape)\n",
    "\n",
    "pred_df = pred_combi_kill_df.loc[[d[0] for d in obs_combi_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % cell death']\n",
    "print (pred_df.shape)\n",
    "\n",
    "scatter_combi_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_combi_df.loc[:, 'Replicate'] = scatter_combi_df['Replicate'].astype(str)\n",
    "print (scatter_combi_df.shape)\n",
    "scatter_combi_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:14.299653Z",
     "start_time": "2020-11-17T13:33:13.893899Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drug combination | 0.28 (1.81e-01)\n",
      "Drug combination [R-sq -94.30%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Drug combination', style='Patient', s=150, alpha=1.0)\n",
    "\n",
    "for _, row in scatter_combi_df.groupby(['Drug combination', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "\n",
    "vmin = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Drug combination | {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Drug combination [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "if dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_combi_drug_{}_{}.svg'.format(dosage_used, model_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Barchart for single vs combi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:14.303919Z",
     "start_time": "2020-11-17T13:33:14.301277Z"
    }
   },
   "outputs": [],
   "source": [
    "# selected_patient = 'HN137' # HN137, HN120, HN148, HN159, HN160"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:14.309112Z",
     "start_time": "2020-11-17T13:33:14.305786Z"
    }
   },
   "outputs": [],
   "source": [
    "# sorted_combi_list = obs_bar_df[obs_bar_df['drug_name']=='C'].groupby('combi_name').mean().sort_values('kill').index\n",
    "# sorted_combi_list = ['Docetaxel\\nGefitinib', 'Doxorubicin\\nVorinostat',\n",
    "#        'Docetaxel\\nEpothilone B', 'Gefitinib\\nEpothilone B',\n",
    "#        'Epothilone B\\nPI-103']\n",
    "sorted_combi_list = ['Docetaxel\\nEpothilone B', 'Docetaxel\\nGefitinib', 'Doxorubicin\\nVorinostat', \n",
    "                     'Epothilone B\\nPI-103', 'Gefitinib\\nEpothilone B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:15.461707Z",
     "start_time": "2020-11-17T13:33:14.310847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1560x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, axes = plt.subplots(figsize=(13, 6), nrows=1, ncols=5, sharey=True, sharex=True)\n",
    "\n",
    "\n",
    "for selected_patient, ax in zip(['HN120', 'HN137', 'HN148', 'HN159', 'HN160'], axes.flatten()):\n",
    "\n",
    "    obs_bar_list = []\n",
    "\n",
    "    for _, row in scatter_combi_df[scatter_combi_df['Patient']==selected_patient].iterrows():\n",
    "        combi_name = row['Drug combination']\n",
    "        combi_kills = row['Observed % cell death']\n",
    "\n",
    "        drug_a, drug_b = combi_name.split('|')\n",
    "        combi_name = '{}\\n{}'.format(drug_a, drug_b)\n",
    "\n",
    "        drug_a_kills = scatter_single_df[(scatter_single_df['Patient']==selected_patient) & (scatter_single_df['Drug']==drug_a)]['Observed % cell death'].values\n",
    "        drug_b_kills = scatter_single_df[(scatter_single_df['Patient']==selected_patient) & (scatter_single_df['Drug']==drug_b)]['Observed % cell death'].values\n",
    "\n",
    "        for drug_a_kill in drug_a_kills:\n",
    "            obs_bar_list += [['A', drug_a_kill, combi_name]]\n",
    "        for drug_b_kill in drug_b_kills:\n",
    "            obs_bar_list += [['B', drug_b_kill, combi_name]]\n",
    "\n",
    "        if type(row['Observed % cell death']) is float:\n",
    "            obs_bar_list += [['C', combi_kills, combi_name]]\n",
    "        else:\n",
    "            for combi_kill in combi_kills:\n",
    "                obs_bar_list += [['C', combi_kill, combi_name]]\n",
    "\n",
    "    obs_bar_df = pd.DataFrame(obs_bar_list, columns=['drug_name', 'kill', 'combi_name'])\n",
    "    clrs = ['orange' if d == 'C' else 'grey' for d in obs_bar_df.groupby(['combi_name', 'drug_name']).median().reset_index()['drug_name'].values]\n",
    "\n",
    "    sns.barplot(data=obs_bar_df, y='combi_name', x='kill', hue='drug_name', order=sorted_combi_list, palette=clrs, \n",
    "                estimator=np.median, ci='sd', ax=ax)\n",
    "    ax.set_xlim((0, 100))\n",
    "\n",
    "    ax.get_legend().remove()\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_yticks([])\n",
    "    \n",
    "    ax.set_title(selected_patient, fontsize=16)\n",
    "    \n",
    "#     if dosage_used == '3 fold':\n",
    "#         ax.set_xlabel('Observed % cell death\\n(3-fold dilution)')\n",
    "#     else:\n",
    "#         ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "    ax.set_xlabel('Observed % cell death')\n",
    "        \n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "\n",
    "fig.savefig('../figure/Fig4_single_combi_{}_all_obs.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.298672Z",
     "start_time": "2020-11-17T13:33:15.464635Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1560x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, axes = plt.subplots(figsize=(13, 6), nrows=1, ncols=5, sharey=True)\n",
    "\n",
    "clrs = ['orange' if d == 'C' else 'grey' for d in obs_bar_df.groupby(['combi_name', 'drug_name']).mean().reset_index()['drug_name'].values]\n",
    "\n",
    "\n",
    "for selected_patient, ax in zip(['HN120', 'HN137', 'HN148', 'HN159', 'HN160'], axes.flatten()):\n",
    "\n",
    "    pred_bar_list = []\n",
    "\n",
    "    for _, row in scatter_combi_df[scatter_combi_df['Patient']==selected_patient].iterrows():\n",
    "        combi_name = row['Drug combination']\n",
    "        combi_kill = row['Predicted % cell death']\n",
    "\n",
    "        drug_a, drug_b = combi_name.split('|')\n",
    "        combi_name = '{}\\n{}'.format(drug_a, drug_b)\n",
    "\n",
    "        drug_a_kill = scatter_single_df[(scatter_single_df['Patient']==selected_patient) & (scatter_single_df['Drug']==drug_a)]['Predicted % cell death'].values.mean()\n",
    "        drug_b_kill = scatter_single_df[(scatter_single_df['Patient']==selected_patient) & (scatter_single_df['Drug']==drug_b)]['Predicted % cell death'].values.mean()\n",
    "\n",
    "        pred_bar_list += [['A', drug_a_kill, combi_name]]\n",
    "        pred_bar_list += [['B', drug_b_kill, combi_name]]\n",
    "        pred_bar_list += [['C', combi_kill, combi_name]]\n",
    "\n",
    "    pred_bar_df = pd.DataFrame(pred_bar_list, columns=['drug_name', 'kill', 'combi_name'])\n",
    "\n",
    "#     sns.set(font_scale=1.25, style='ticks')\n",
    "#     fig, ax = plt.subplots(figsize=(7, 3))\n",
    "\n",
    "#     clrs = ['orange' if d == 'C' else 'grey' for d in pred_bar_df['drug_name'].values]\n",
    "#     sns.barplot(data=pred_bar_df, x='combi_name', y='kill', hue='drug_name', order=sorted_combi_list, palette=clrs, ci=None, ax=ax)\n",
    "    \n",
    "    sns.barplot(data=pred_bar_df, y='combi_name', x='kill', hue='drug_name', order=sorted_combi_list, palette=clrs, \n",
    "                estimator=np.median, ci=None, ax=ax)\n",
    "    ax.set_xlim((0, 100))\n",
    "\n",
    "    ax.get_legend().remove()\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_yticks([])\n",
    "    ax.set_xlabel('\\nPredicted % cell death')\n",
    "\n",
    "    ax.set_title(selected_patient, fontsize=16)\n",
    "    \n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_single_combi_all_pred.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.309579Z",
     "start_time": "2020-11-17T13:33:16.300477Z"
    }
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-384-a871fdc9ebee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "assert False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare improvement of drug combi over single drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.311412Z",
     "start_time": "2020-11-17T13:33:12.723Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_combi_imp_df = pred_combi_df[['patient', 'Combi Name', 'improve']].pivot(index='Combi Name', columns='patient', values='improve')\n",
    "pred_combi_pimp_df = pred_combi_df[['patient', 'Combi Name', 'improve_p']].pivot(index='Combi Name', columns='patient', values='improve_p')\n",
    "\n",
    "pred_combi_imp_df = pred_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "pred_combi_pimp_df = pred_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.312771Z",
     "start_time": "2020-11-17T13:33:12.724Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_combi_imp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.313940Z",
     "start_time": "2020-11-17T13:33:12.725Z"
    }
   },
   "outputs": [],
   "source": [
    "# TEMPORARY: using averge response\n",
    "\n",
    "temp_df = obs_kill_df.loc[combi_drug_list].reset_index().drop(['File name', 'Replicate'], axis=1)\n",
    "temp_df = temp_df.groupby('Drug').median()\n",
    "# temp_df = temp_df.loc[~temp_df.index.duplicated(keep='first')]\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.315192Z",
     "start_time": "2020-11-17T13:33:12.725Z"
    }
   },
   "outputs": [],
   "source": [
    "imp_results = []\n",
    "pimp_results = []\n",
    "for c, data in temp_df.loc[combi_drug_list, patient_list].iterrows():\n",
    "    a, b = c.split('|')\n",
    "    \n",
    "    c_kill = data.values\n",
    "#     best_kill = obs_kill_df.loc[[a, b]].max()[patient_list]\n",
    "    best_kill = obs_kill_df.loc[[a, b]].reset_index().groupby('Drug').median()[patient_list].max()\n",
    "    \n",
    "#     print (c, c_kill, best_kill)\n",
    "    \n",
    "    imp = list((c_kill - best_kill).values)\n",
    "    pimp = list(((c_kill - best_kill)/best_kill).values)\n",
    "    \n",
    "    imp_results += [[c] + imp]\n",
    "    pimp_results += [[c] + pimp]\n",
    "        \n",
    "obs_combi_imp_df = pd.DataFrame(imp_results)\n",
    "obs_combi_imp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_imp_df = obs_combi_imp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_pimp_df = pd.DataFrame(pimp_results)\n",
    "obs_combi_pimp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_imp_df = obs_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.316432Z",
     "start_time": "2020-11-17T13:33:12.726Z"
    }
   },
   "outputs": [],
   "source": [
    "obs_kill_df.loc[[a, b]].reset_index().groupby('Drug').median()[patient_list].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.317679Z",
     "start_time": "2020-11-17T13:33:12.727Z"
    }
   },
   "outputs": [],
   "source": [
    "obs_combi_imp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.318928Z",
     "start_time": "2020-11-17T13:33:12.728Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "obs_df = obs_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'Patient', 'Observed % death increase']\n",
    "\n",
    "pred_df = pred_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % death increase']\n",
    "\n",
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "scatter_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "\n",
    "sns.scatterplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', hue='Drug combination', style='Patient', s=100, alpha=0.7)\n",
    "# sns.regplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', x_ci='ci', ci=99, scatter=False, color='grey')\n",
    "# plt.plot([0, 25], [0, 25], ls=\"--\", c=\".3\")\n",
    "\n",
    "vmin = scatter_df[['Observed % death increase', 'Predicted % death increase']].min().min()\n",
    "vmax = scatter_df[['Observed % death increase', 'Predicted % death increase']].max().max()\n",
    "\n",
    "# ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "# ax.set_xlim((vmin-5, vmax+5))\n",
    "# ax.set_ylim((vmin-5, vmax+5))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0)\n",
    "\n",
    "scor, pval = stats.pearsonr(obs_df['Observed % death increase'].values, pred_df['Predicted % death increase'].values)\n",
    "ax.set_title('Single VS Combi\\n(Pearson r = {:.2f} p-val < {:.2e})'.format(scor, pval))\n",
    "\n",
    "# r2 = metrics.r2_score(scatter_df['Observed % death increase'].values, scatter_df['Predicted % death increase'].values)\n",
    "# ax.set_title('Single VS Combi [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "plt.tight_layout()\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_scatter.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.320143Z",
     "start_time": "2020-11-17T13:33:12.729Z"
    }
   },
   "outputs": [],
   "source": [
    "scatter_df.sort_values('Predicted % death increase', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.321361Z",
     "start_time": "2020-11-17T13:33:12.730Z"
    }
   },
   "outputs": [],
   "source": [
    "scatter_df['Observed % death increase'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.322650Z",
     "start_time": "2020-11-17T13:33:12.731Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def change_boxplot_edge_color(ax, col):\n",
    "    for i, artist in enumerate(ax.artists):\n",
    "        # Set the linecolor on the artist to the facecolor, and set the facecolor to None\n",
    "        artist.set_edgecolor(col)\n",
    "        # artist.set_facecolor('None')\n",
    "\n",
    "        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)\n",
    "        # Loop over them here, and use the same colour as above\n",
    "        for j in range(i*6,i*6+6):\n",
    "            line = ax.lines[j]\n",
    "            line.set_color(col)\n",
    "            line.set_mfc(col)\n",
    "            line.set_mec(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.323876Z",
     "start_time": "2020-11-17T13:33:12.732Z"
    }
   },
   "outputs": [],
   "source": [
    "cutoff = 7.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.325142Z",
     "start_time": "2020-11-17T13:33:12.733Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.1, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(3, 4))\n",
    "\n",
    "merge_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "merge_df.loc[:, 'Observed increase'] = merge_df['Observed % death increase'] >= cutoff\n",
    "\n",
    "sns.boxplot(data=merge_df, x='Observed increase', y='Predicted % death increase', fliersize=4, color='lightblue', ax=ax)\n",
    "# sns.swarmplot(data=merge_df, x='Observed increase', y='Predicted % death increase', color='black', alpha=0.5, ax=ax)\n",
    "change_boxplot_edge_color(ax, 'black')\n",
    "ax.set_xlabel('Observed increase ' + r'$\\geq {}\\%$'.format(cutoff))\n",
    "\n",
    "x = merge_df.loc[merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "y = merge_df.loc[~merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "\n",
    "sns.despine()\n",
    "print (\"{} {:.2e}\\n{} {:.2e}\".format(*stats.ranksums(x, y), *stats.ttest_ind(x, y)))\n",
    "plt.tight_layout()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_{}_median_cutoff_{}.svg'.format(dosage_used, cutoff, model_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.326379Z",
     "start_time": "2020-11-17T13:33:12.734Z"
    }
   },
   "outputs": [],
   "source": [
    "merge_df.sort_values(['Observed increase', 'Drug combination'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.327600Z",
     "start_time": "2020-11-17T13:33:12.734Z"
    }
   },
   "outputs": [],
   "source": [
    "print (\"{:.2f} ({:.2e})\".format(*stats.ranksums(x, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.328885Z",
     "start_time": "2020-11-17T13:33:12.735Z"
    }
   },
   "outputs": [],
   "source": [
    "obs = (merge_df['Observed % death increase'].values >= cutoff).astype(int)\n",
    "pred = (merge_df['Predicted % death increase'].values >= cutoff).astype(int)\n",
    "pred_val = merge_df['Predicted % death increase'].values\n",
    "\n",
    "Counter(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.330116Z",
     "start_time": "2020-11-17T13:33:12.736Z"
    }
   },
   "outputs": [],
   "source": [
    "metrics.accuracy_score(obs, pred), metrics.f1_score(obs, pred, pos_label=1), metrics.f1_score(obs, pred, pos_label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:33:16.331330Z",
     "start_time": "2020-11-17T13:33:12.737Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "precision, recall, thresholds = metrics.precision_recall_curve(obs, pred_val)\n",
    "plt.plot(recall, precision)\n",
    "\n",
    "print (metrics.auc(recall, precision))\n",
    "\n",
    "no_skill = len(obs[obs==1]) / len(obs)\n",
    "plt.axhline(y=no_skill)\n",
    "\n",
    "plt.xlim((0,1))\n",
    "plt.ylim((0,1.1))\n",
    "\n",
    "# fig.savefig('../figure/Fig4_pr_curve_{}_{}.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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